scholarly journals Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maude Wagner ◽  
Francine Grodstein ◽  
Karen Leffondre ◽  
Cécilia Samieri ◽  
Cécile Proust-Lima
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Measurement Errors ◽  
Mixed Model ◽  
Time Window ◽  
Time Windows ◽  
Critical Time ◽  
Cumulative Exposure ◽  
Exposure Measurement ◽  
Exposure History

Abstract Background Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. Methods We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. Results A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease). Conclusions This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

scholarly journals Time-Varying Associations Between an Exposure History and a Subsequent Health Outcome: A landmark Approach to Identify Critical Windows.

2021 ◽  
Author(s):  
Maude Wagner ◽  
Francine Grodstein ◽  
Karen Leffondre ◽  
Cécilia Samieri ◽  
Cécile Proust-Lima
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Measurement Errors ◽  
Mixed Model ◽  
Time Window ◽  
Time Windows ◽  
Critical Time ◽  
Cumulative Exposure ◽  
Exposure Measurement ◽  
Exposure History

Abstract Background: Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology.Methods: We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model.Results: A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease).Conclusions: This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Blindness short after treatment of acute primary angle closure in China

2020 ◽  
pp. bjophthalmol-2020-316259
Author(s):  
Shuning Li ◽  
Guangxian Tang ◽  
Su Jie Fan ◽  
Gang Zhai ◽  
Jianhua Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Time Window ◽  
Case Series ◽  
Critical Time ◽  
Education Level ◽  
Angle Closure ◽  
Retrospective Case ◽  
Acute Primary Angle Closure ◽  
Factors Associated ◽  
Primary Angle Closure

AimsTo study the risk factors associated with blindness after treatment of acute primary angle closure (APAC), and to identify the critical time window to decrease rate of blindness.MethodsIn this multicentre retrospective case series, 1030 consecutive subjects (1164 eyes) with APAC in China were recruited. The rates of blindness were analysed up to 3 months after treatment of APAC. A logistic regression was used to identify the risk factors associated with blindness, including age, gender, distance to hospital, rural or urban settings, treatment method, education level, time from symptom to treatment (TST, hours) and presenting intraocular pressure (IOP). The critical time window associated with a blindness rate of ≤1% was calculated based on a cubic function by fitting TST to the rate of blindness at each time point.ResultsThe rate of blindness after APAC was 12.54% after treatment. In multivariate regression, education level, TST and presenting IOP were risk factors for blindness (p=0.022, 0.004 and 0.001, respectively). The critical time window associated with a blindness rate of ≤1% was 4.6 hours.Conclusions and relevanceEducation level, TST and presenting IOP were risk factors for blindness after APAC. Timely medical treatment is key in reducing blindness after APAC.

scholarly journals 610Official statistics on health outcomes and risk factors for small geographic areas

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Ian Rayson
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Health Outcomes ◽  
Survey Data ◽  
Health Survey ◽  
Mixed Model ◽  
Local Level ◽  
Outcome Variable ◽  
Local Health ◽  
Health Statistics

Abstract Background The 2017-18 National Health Survey (NHS) is an Australia-wide detailed health survey conducted by the Australian Bureau of Statistics (ABS). Although the survey enables reliable National and State official health statistics, the sample size is too small to produce reliable data for smaller population areas. To produce such data the ABS applies an innovative Small Area Estimation (SAE) approach, combining the survey data and several population data sources. Methods We predict prevalence of each health outcome variable by fitting a logistic mixed model. The modelled NHS data are enhanced by data from the ABS 2016 Census, Estimated Resident Population, and several administrative sources including Medical and Pharmaceutical transactions. Models are selected using a bespoke stepwise selection process; where the predictor variables have a strong association with the health outcome, whilst also ensuring that the estimated rates maintain consistency with published national data for that health outcome. Results Health statistics were produced for over 25 health outcomes and risk factors for 1134 Population Health Areas (PHAs) across Australia. The data show significant variation in rates between areas that are not evident in National and State level data. For example, the prevalence of adult current smokers in PHAs ranged from 4.4% to 34.6%, compared to 15.1% nationally. Conclusions The ABS SAE approach is an innovative method that enables production of reliable official health statistics, meeting a known data gap of local level health data. Key messages The ABS SAE approach delivers reliable official local health statistics, meeting an important data need not met using survey data alone.

scholarly journals Relationship Between Yearly Fluctuations in Fusarium Head Blight Intensity and Environmental Variables: A Window-Pane Analysis

2010 ◽  
Vol 100 (8) ◽  
pp. 784-797 ◽  
Author(s):  
A. B. Kriss ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Fusarium Head Blight ◽  
Environmental Variables ◽  
Time Window ◽  
Time Windows ◽  
Rank Correlation ◽  
Critical Time ◽  
Fungal Colonization ◽  
Head Blight ◽  
Starting Time ◽  
Window Pane

Window-pane methodology was used to determine the length and starting time of temporal windows where environmental variables were associated with annual fluctuations of Fusarium head blight (FHB) intensity in wheat. Initial analysis involved FHB intensity observations for Ohio (44 years), with additional analyses for Indiana (36 years), Kansas (28 years), and North Dakota (23 years). Selected window lengths of 10 to 280 days were evaluated, with starting times from approximate crop maturity back to the approximate time of planting. Associations were quantified with Spearman rank correlation coefficients. Significance for a given variable (for any window starting time in a collection of starting times) was declared using the Simes' multiplicity adjustment; at individual time windows, significant correlations were declared when the individual (unadjusted) P values were <0.005. In all states, moisture- or wetness-related variables (e.g., daily average relative humidity [RH] and total daily precipitation) were found to be positively correlated with FHB intensity for multiple window lengths and starting times; however, the highest correlations were primarily for shorter-length windows (especially 15 and 30 days) at similar starting times during the final 60 days of the growing season, particularly near the time of anthesis. This period encompasses spore production, dispersal, and fungal colonization of wheat spikes. There was no evidence of significant correlations between FHB and temperature-only variables for any time window; however, variables that combined aspects of moisture or wetness with temperature (e.g., duration of temperature between 15 and 30°C and RH ≥ 80%) were positively correlated with FHB intensity. Results confirm that the intensity of FHB in a region depends, at least in part, on environmental conditions during relatively short, critical time periods for epidemic development.

scholarly journals Effects of fenofibrate therapy on renal function in primary gout patients

Rheumatology ◽  
2021 ◽  
Author(s):  
Xinde Li ◽  
Wenyan Sun ◽  
Jie Lu ◽  
Yuwei He ◽  
Ying Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Treatment Period ◽  
Mixed Model ◽  
Interaction Analysis ◽  
Serum Urate ◽  
Factors Affecting ◽  
Primary Gout ◽  
Potential Risk Factors ◽  
Multivariable Regression ◽  
Risk Predictors

Abstract Objective To investigate the incidence and potential risk factors for development of fenofibrate-associated nephrotoxicity in gout patients. Methods A total of 983 gout patients on fenofibrate treatment who visited the dedicated Gout Clinic at the Affiliated Hospital of Qingdao University between September 2016 and June 2020 were retrospectively enrolled from the electronic records system. Fenofibrate-associated nephrotoxicity was defined as an increase in serum creatinine (SCr) ≥0.3 mg/dl within 6 months of fenofibrate initiation. The change trend of SCr and uric acid levels during the treatment period were assessed by a generalised additive mixed model (GAMM). Multivariate analysis was performed for risk factors affecting elevated SCr. Results A total of 100 (10.2%) patients experienced an increase in SCr ≥0.3 mg/dl within 6 months after fenofibrate initiation. The median change of SCr in the whole cohort was 0.11 mg/dl [interquartile range (IQR) 0.03–0.20], whereas it was 0.36 (0.33–0.45) in the fenofibrate-associated nephrotoxicity group. In a multivariable regression model, chronic kidney disease (CKD) [odds ratio (OR) 2.39 (95% CI 1.48, 3.86)] and tophus [OR 2.29 (95% CI 1.39, 3.78)] were identified to be risk predictors, independent of measured covariates, of fenofibrate-associated nephrotoxicity. During the treatment period, although SCr temporarily increased, serum urate and triglyceride concentrations decreased using the interaction analysis of GAMM. Of those with fenofibrate withdrawal records, the SCr increase in 65% of patients was reversed after an average of 49 days off the drug. Conclusions This observational study implied that fenofibrate-associated nephrotoxicity occurs frequently in gout patients, especially in patients with tophi or CKD. The potential renal risks of fenofibrate usage in gout needs additional research.

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

scholarly journals The last-mile vehicle routing problem with delivery options

OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Tilk ◽  
Katharina Olkis ◽  
Stefan Irnich
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Service Level ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Common Location ◽  
Delivery Options

AbstractThe ongoing rise in e-commerce comes along with an increasing number of first-time delivery failures due to the absence of the customer at the delivery location. Failed deliveries result in rework which in turn has a large impact on the carriers’ delivery cost. In the classical vehicle routing problem (VRP) with time windows, each customer request has only one location and one time window describing where and when shipments need to be delivered. In contrast, we introduce and analyze the vehicle routing problem with delivery options (VRPDO), in which some requests can be shipped to alternative locations with possibly different time windows. Furthermore, customers may prefer some delivery options. The carrier must then select, for each request, one delivery option such that the carriers’ overall cost is minimized and a given service level regarding customer preferences is achieved. Moreover, when delivery options share a common location, e.g., a locker, capacities must be respected when assigning shipments. To solve the VRPDO exactly, we present a new branch-price-and-cut algorithm. The associated pricing subproblem is a shortest-path problem with resource constraints that we solve with a bidirectional labeling algorithm on an auxiliary network. We focus on the comparison of two alternative modeling approaches for the auxiliary network and present optimal solutions for instances with up to 100 delivery options. Moreover, we provide 17 new optimal solutions for the benchmark set for the VRP with roaming delivery locations.

Identifying patients with CKD risk at the time of nephrectomy: When to initiate nephrology consult

2021 ◽  
pp. 239936932110319
Author(s):  
Yihe Yang ◽  
Zachary Kozel ◽  
Purva Sharma ◽  
Oksana Yaskiv ◽  
Jose Torres ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
Radical Nephrectomy ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interstitial Fibrosis ◽  
Kidney Neoplasm ◽  
Tubular Atrophy ◽  
Independent Risk Factors

Introduction: The prevalence of chronic kidney disease (CKD) is high among kidney neoplasm patients because of the overlapping risk factors. Our purpose is to identify kidney cancer survivors with higher CKD risk. Methods: We studied a retrospective cohort of 361 kidney tumor patients with partial or radical nephrectomy. Linear mixed model was performed. Results: Of patients with follow-up >3 months, 84% were identified retrospectively to fulfill criteria for CKD diagnosis, although CKD was documented in only 15%. Urinalysis was performed in 205 (57%) patients at the time of nephrectomy. Multivariate analysis showed interstitial fibrosis and tubular atrophy (IFTA) >25% ( p = 0.005), severe arteriolar sclerosis ( p = 0.013), female gender ( p = 0.024), older age ( p = 0.012), BMI ⩾ 25 kg/m2 ( p < 0.001), documented CKD ( p < 0.001), baseline eGFR ⩽ 60 ml/min/1.73 m2 ( p < 0.001), and radical nephrectomy ( p < 0.001) were independent risk factors of lower eGFR at baseline and during follow-up. Average eGFR decreased within 3 months post nephrectomy. However, patients with different risk levels showed different eGFR time trend pattern at longer follow-ups. Multivariate analysis of time × risk factor interaction showed BMI, radical nephrectomy and baseline eGFR had time-dependent impact. BMI ⩾ 25 kg/m2 and radical nephrectomy were associated with steeper eGFR decrease slope. In baseline eGFR > 90 ml/min/1.73 m2 group, eGFR rebounded to pre-nephrectomy levels during extended follow-up. In partial nephrectomy patients with baseline eGFR ⩾ 90 ml/min/1.73 m2 ( n = 61), proteinuria ( p < 0.001) and BMI ( p < 0.001) were independent risk factors of decreased eGFR during follow up. Conclusions: As have been suggested by others and confirmed by our study, proteinuria and CKD are greatly under-recognized. Although self-evident as a minimum workup for nephrectomy patients to include SCr, eGFR, urinalysis, and proteinuria, the need for uniform applications of this practice should be reinforced. Non-neoplastic histology evaluation is valuable and should include an estimate of global sclerosis% (GS) and IFTA%. Patients with any proteinuria and/or eGFR ⩽ 60 at the time of nephrectomy or in follow-up with urologists, and/or >25% GS or IFTA, should be referred for early nephrology consultation.

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